---
# System prepended metadata

title: Rebuttal 2

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# Rebuttal 2



# Note to ACs [to post]

Dear Area Chairs,

We hope this message finds you well. We wanted to let you know that based on the initial responeses from reviewers, we have made significant revisions to our manuscript and have incorporated all suggested experiments. Reviewers requested more experiments, and we've responded with seven long-horizon experiments, each run 10 times. Our results show our approach's effectiveness (**88.6%** completion rate), surpassing the state of the art, Language to Reward [1], which achieved only a 24.3% completion rate. Moreover, we have posted those revisions on Nov 18th but we have recieved limited engagement from the reviewers. We received feedback from only three of four of the reviewers, given to us the night before the rebuttal deadline. We are uncertain about any remaining issues or areas for further improvement before the author response deadline.

We kindly ask for the following during the reviewer-AC discussion period:

1. Re-evaluation of Ratings: Considering the significant changes made to the manuscript, we request that the ratings be re-evaluated. 
2. Explanation of Ratings Post-Rebuttal: We request that the reviewers be asked to provide a clear explanation for their ratings after the rebuttal. 


Additionally, we would like to mention that some of the existing reviewer feedback isn't clear. For example, Reviewer b9qX wrote that they hope our manuscript is updated and is open to increasing their score by one point during the reviewer discussions, but they didn't write any remaining questions or concerns that prevent them from increasing it further. In the latest response from Reviewer Ya9u, it remains unclear whether they have accurately grasped the content of the paper. The reviewer mentioned that "the current claimed contribution is that the framework also enables replanning at the low-level actions," however, this is not our contribution, and we have not stated that anywhere in the paper. Another quote from the same reviewer is "the claim of the paper can be rephrased to only focusing on high-level replanning using VLMs." However, this statement of the reviewer is incorrect, as we highlighted to the reviewer. We took careful attention, updating the contributions of the paper to be clear.

We would like to kindly ask the AC to consider our revisions during the rebuttal stage when making the decision. We believe we have addressed all the concerns and points raised by the reviewers.

Thank you!

All the best,
Authors

[1] Wenhao Yu et al, “Language to Rewards for Robotic Skill Synthesis”. (2023)


# [all reviewers] summary of the results [to finish, to post]

Dear Reviewers, 

In light of the earlier feedback and supplementary experiments conducted, the subsequent table provides a summary of our results:


**Table 1: Number of times that the models completed Tasks 1-7 out of 10 runs. Average completion rates across 7 tasks are listed in the last column.**

| Model                        | Task 1  | Task 2  | Task 3 | Task 4 | Task 5 | Task 6 | Task 7 | Average  |
|------------------------------|---------|---------|--------|--------|--------|--------|--------|----------|
| Our Model [full]             | **100%**| **100%**| **60%**| **80%**| **100%**| 90%    | **90%** | **88.6%**|
| Our Model [no Verifier]      | 80%     | 80%     | 20%    | 60%    | **100%**| **100%**| 80%     | 74.3%    |
| Our Model [no Perceiver]     | 90%     | 90%     | 30%    | 20%    | 50%    | 20%    | 0%     | 42.9%    |
| Our Model [no Replan]        | 80%     | 70%     | 0%     | 0%     | 0%     | 80%    | 10%    | 34.3%    |
| Language to Rewards [1]      | 90%     | 20%     | 0%     | 0%     | 0%     | 50%    | 10%    | 24.3%    |

The videos of our experiments are available at our website:
https://sites.google.com/view/replan-iclr/home


## Long-Horizon Tasks

In addition to the seven tasks in the table above, we introduce an additional long-horizon task here.

**Task 8 (Figure A.1o, A.1p, video available in our website)** In this task, the robot is asked to open a slide cabinet, which is locked via a weight sensor. The weight is hidden in another cabinet, with a microwave as a distraction option. The robot needs to first search for the weight by opening the microwave and the cabinet, then retrieve the weight out of the cabinet, put the weight onto the weight sensor and eventually unlock and open the slide cabinet.

**Result for Task 8** RePLan achieves **70%** completion rate out of 10 runs while Language to Rewards completes 0%.

[1] Wenhao Yu et al, “Language to Rewards for Robotic Skill Synthesis”. (2023)


# Response to Reviewer jtHn [posted]

Dear Reviewer jtHn,

We hope that we have responded to all of your concerns. We welcome any feedback you have in light of our latest revisions, and if you have any additional questions, please let us know!

Cheers,
Authors

# Response to Reviewer b9qX [posted]

Dear Reviewer b9qX,

Thank you for considering our work and for your openness to increasing the score. To help us refine our work further, can you indicate any particular aspects of the paper that, if improved, would make you more inclined to consider a higher score? We would really appreciate any insights on how we can improve our work or address any remaining concerns. Thank you in advance for your guidance.

Best regards,
Authors

# Response to Reviewer Ya9u [posted]

Dear Reviewer Ya9u,

Thank you for checking our responses and for increasing your score.

We kindly wish to bring to the reviewer's attention that our contribution is a framework that can handle long-term, complex multi-stage tasks, which consists of four essential components:

- using perceiver models for high-level replanning,
- creating hierarchical plans with language models,
- verifying outputs from these language models,
- robot behavior through reward generation. 

While high-level replanning is one of the major compoments of our proposed method, RePLan is an **end-to-end framework** that solves long-horizon planning tasks and outputs low-level robot controls (7-dof joint and gripper values in the case of Franka arms)


- Regarding the first point about ground-truth data, the reviewer stated that "one can easily just use this (ground-truth data) to feed into a text-only LLM to reason about replanning actions". We observe, after doing experiments, that this is not the case. We show an example below that using an LLM and ground-truth data only is not sufficient to complete our relanning task, as opposed to using VLM with scene images (which we also added to Appendix C.4). Even though in theory ground-truth data should provide enough information to understand the scene by the Planner, as far as we understand, this has not been backed up by sufficient evidence and we have not found it to be the case either. Integrating the Perceiver allows the Planner to access visual information that it cannot easily determine from positions and angles alone. If the reviewer has some evidence supporting that ground-truth data is sufficient to "easily" perform high-level replanning and multi-step reasoning, we are happy to perform a comparision between our method and the ones using only ground-truth data. The example is as follows: 

```
A robot is in a simulation environment where it can interact with any object like in the real world.
The robot would like to open the wooden cabinet but it cannot.
Is there something in this scene preventing that, other than the robot? Assume the robot can interact with anything.
These are the names of the objects in our scene: wooden cabinet, red block, yellow cube.
The data of the objects are:
{
    "joints": {
        "wooden_cabinet": 0.16235716765387215
    },
    "objects": {
        "wooden_cabinet_handle": [0.08292539, 0.50163332, 0.21],
        "target_position_in_wooden_cabinet": [0.2,    0.85,   0.0775],
        "red_block_right_side": [0.2158331,  0.55110136, 0.15143364],
        "yellow_cube": [-0.24545329,  0.01631876,  0.03599184],
        "palm": [0.2231031m  -0.00875834m  0.76900653]
    }
}
```

```
The data does not indicate any object that would prevent the robot from opening the cabinet.
There's no information about any object blocking the cabinet or its handle.
The problem could be due to the robot itself as it might not possess the necessary functionality to interact with the objects as intended.
The cabinet's joint also has a non-zero value, which might mean it's not locked or stuck.
Hence, there is likely a mechanical limitation with the robot or an issue with its programming.
```

- Regarding the second point concerning the sim2real gap and the application of our method in real-world scenarios, it's important to clarify that we do not rely on ground-truth data for high-level replanning, a key aspect of our contribution. While we do employ ground-truth data for low-level robot motion planning, we acknowledge the limitation that this may impose on the applicability of RePlan in real-world experiments. It's worth noting that many papers, as the reviewer is aware, have focused on object pose estimation for simple objects in the context of low-level robot motion planning. These existing approaches can be integrated into our system. However, for more intricate scenarios, such as grasping a blocker on a cabinet door, advanced techniques are necessary to comprehend object affordances for manipulation. For instance, in the work by L. Wang et al., titled "Self-Supervised Learning of Action Affordances as Interaction Modes" (ICRA 2023), the authors delve into such complexities. The examples cited in [1] (Language-to-Reward) involve tasks like picking a cube/apple and opening a drawer. We posit that addressing the challenge of utilizing visual data for robot motion planning in intricate, contact-rich manipulation scenarios, as in our case, demands dedicated research efforts.


- Regarding the third point, we apologize if our contribution description was unclear. We have revised it to explicitly state that replanning occurs exclusively at the high-level task planning. It's important to clarify that while our method is end-to-end, covering high-level task description to low-level motion generation, there is no explicit replanning at the low-level. Upon thorough review, we confirmed that our manuscript does not make any claims about replanning at the low-level motion of the robot. Our approach for low-level reward generation for Model Predictive Control (MPC) draws inspiration from the work in [1] (Language to Rewards).Upon careful examination of [1], it appears they do not engage in replanning at the motion level. Instead, their motion policy generator operates in a closed-loop (reactive) manner, aligning with our approach.



Thank you once more for your feedback. Please feel free to ask any additional questions for further clarification.

Best,
Authors

<!-- with only -->
<!-- Our paper proposed an the end-to-end framework that solves long-horizon planning tasks to robot low-level 7-dof joint and gripper values. While high-level replanning is one of major compoments of our proposed method, RePlan uses reward genration for low-level motion plan that can handle to complex manipulation tasks. -->


<!-- First of all, We would like to remind the reviewer that our contribution involves four key components: -->

<!-- - Regarding the second point for sim2real gap and transferring the method to real-world scenarios, we are not using ground-truth data for the high-level replanning, where one of our contribution lies. However, we use at low-level robot motion plan. We agree with the reviewer that this limiation may limit the usability of RePlan on real-world experiment. We would like to note that, many papers, as reviewer is aware of, have been working on object pose estimation of simple objects for low-level robot motion planning. These approaches can be integrated with our system. However, for more complicated scnarios, for example grasping the blocker of the cabinet door, more advanced techniques are required to understand the object affordances for manipulation (for example, L. Wang, et. al., "Self-Supervised Learning of Action Affordances as Interaction Modes", ICRA 2023). The examples provided in [1] (Language-to-Reward) are two tasks, picking a cube/apple, openning a drawer. We believe addressing the problem of using visual data for robot motion planning for complicated contact-rich manipulation scenarios (which is our case), requires a dedicated work.
 -->
<!-- Based on the behavior of the robot from the experiment video, those experiments have been performed  in the cartesian space with the robot end-effector position/linear velocity as the output of the policy. -->
<!-- In our case, the output of MPC is the robot joint values and gripper value. -->
<!-- We would like to highlight that in other works, for example behavior-1k, they use magic grasp for interacting with object in environment, which is not our case. -->



<!-- - Regarding the third point, we appologize if the description of our contirbuition was not clear. we have updated our contribution description and made it clear than replanning only happen for the high-level task planning.
we double check our manuscript to ensure we didn't make any claim of having replanning behavior at low-level motion of robot. However, our method, is end-to-end, from high-level task description to low-level motion generation. Our low-level approach, reward generation for MPC, as mentioned in the paper, has been inspired by the work [1] (Language-to-reward).
We have also double-check carefully the paper [1].
Based on our understanind, they are not doing any replanning at motion level. However, their motion policy generator is closed-loop (reactive), similar to our case. -->

<!-- Our replanning only happens at high-level and -->

<!-- We have updated the contribution, so that it is better reflected in the paper. -->

<!-- () eplanning with onlyWhile I agree with authors' response that prior works (e.g., [1]) also assume the ground-truth model is available, these works do show the feasibility of transferring to the real world (or even just image-based observations in sim) on certain tasks. But currently this is not shown in the paper. -->





<!-- Thank you very much for replying, and we are glad that we have addressed most of your concerns. 

If there is a bar on the handle, we do not know if the bar is inside the handle

While we have the position and joint angles, 
 -->

# Response to Reviewer 6UHr [Posted]

Dear Reviewer 6UHr,

Thank you for checking our responses and thank you for increasing your score.  We agree with you and Reviewer b9qX that the Perceiver analysis was insufficient in the previous version of the paper. We have added a detailed analysis of the Perceiver in Appendix C. We have also added extensive explanation of the VLM Perceiver in the paper and how RePlan leverages the VLM for long-horizon planning.

Specifically, we show a **quantitative analysis** that measures the performance of **three state-of-the-art VLMs (Qwen, Llava, GPT-4V)** in two tasks: **object recognition and object reasoning**. We provide the results below and also in Append C2. We also show a **qualitative anlysis** below and in Appendix C3 that demonstrates sample VLM inputs/ouputs and errors. Finally, we show a **motivating example** of why constrained VLM prompting is needed since GPT-4V is not able to solve the tasks out-of-the-box.

VLMs are known to be good for object detection, however their reasoning capabilities are not clear. In order to avoid relying on a single VLM prompt, we use several prompts and ask an LLM to create a consensus from their outputs based on the LLM's knowledge of the scene and robot goals. This also allowed the LLM to consider the consistency among the VLM replies (e.g. if 3/6 VLM outputs identify the same problem in the scene, it is more likely that those outputs are reflective of the scene). The motivation behind this was that VLMs are good at captioning images in VQA settings -- in our setting, we ask it to "caption" a problem. This combination allowed for effective reasoning capabilities.

**QUANTITATIVE ANALYSIS:**

We do this study because our Perceiver has two fundamental roles: object detection and failure diagnosis.  Object detection refers to the percentage of objects the VLM is able to recognize in the scene. For object reasoning, we gave the VLM 6 prompts related to failure detection (which we show below and in Appendix B, Figure 9). During the RePLan pipeline, we prompt the VLM with all 6 prompts and then ask the LLM High-Level Planner (which has better reasoning skills) to, given the 6 answers and the LLM's knowledge of the scene (e.g. what objects are present, that a robot is doing a task in a simulation) to provide the most likely reason for why the robot can’t do the task (this prompt is shown below and in Appendix B, Figure 12). First, we report the percentage of responses from the VLM that encompass the true reason out of the 6 inputs for the robot not being able to do the action. Then, we report the ability of the LLM to summarize the VLM outputs into a correct/likely reason as to why the robot cannot perform the task. We repeated each ablation 5 times. 

## Task 3

| Scenarios                                 | Qwen + SAM | Qwen  | Llava | GPT-4V |
|-------------------------------------------|------------|-------|-------|--------|
| VLM object detection                      | $100\%$    | $66\%$| $100\%$ | $100\%$ |
| VLM Reasoning                             | $67\%$     | $0\%$ | $23\%$ | $100\%$ |
| LLM summarization and consistency step    | $100\%$    | $0\%$ | $100\%$ | $100\%$ |


## Task 4

| Scenarios                                 | Qwen + SAM | Qwen  | Llava | GPT-4V |
|-------------------------------------------|------------|-------|-------|--------|
| VLM object detection                      | $100\%$    | $100\%$| $100\%$ | $100\%$ |
| VLM Reasoning                             | $50\%$     | $66\%$| $40\%$ | $83\%$ |
| LLM summarization and consistency step    | $100\%$    | $100\%$| $20\%$ | $100\%$ |

<!-- *Table \ref{tab:VLM_ablation}: VLM ablation study.*-->

We found that Qwen somtimes struggled with object detection of smaller objects, and so we coupled it with Segment Anything Model (SAM) [1] to first segment the objects in the scene. We found that all models did well with object recognition (except for Qwen when not used with SAM). For object reasoning, Qwen + SAM was able to get the correct scene error in 50-67\% of the prompts it was given, and then the LLM was able to summarize the prompts to generate the correct error reason overall. The reason the LLM was able to do this despite the VLM not giving perfect answers was that the remaining VLM answers pertained where the robot was located or a general comment about the objects in the scene. Llava tended to reply that it was unable to reason because the scene was a simulation and not real life. GPT-4V had the best overall performance in all categories, but API calls to it are still restricted. 

**QUALITATIVE ANALYSIS:**

We also show some examples below of prompts to the VLM and its responses, as well as the LLM summary. We will include them in the draft before the deadline, but are posting them here now:

**Task 3: VLM is asked to diagnose why the robot cannot open the cabinet.**

Input to VLM: 
```
1. A robot is in a simulation environment where it can interact with any object like in the real world. The robot would like to open the wooden cabinet but it cannot. Is there something in this scene preventing that, other than the robot? Assume the robot can interact with anything. These are the names of the objects in our scene: wooden cabinet, red block, yellow cube
2. In a simulation, a robot wants to open the wooden cabinet but can't. Is anything else, besides the robot, blocking it? Check the objects in the scene: wooden cabinet, red block, yellow cube.
3. Robot in a simulation wants to open the wooden cabinet, can't. Something else stopping it? Objects in scene: wooden cabinet, red block, yellow cube.
4. A robot can engage with any item. It wants to open the wooden cabinet but can't. Is an object in this scene, apart from the robot, hindering it? Objects present: wooden cabinet, red block, yellow cube
5. I would like to open the wooden cabinet but I cannot. Is there something in this scene preventing that, other than the robot? These are the objects in the scene: wooden cabinet, red block, yellow cube
6. I would like to open the wooden cabinet but I am unable to. Is there something in this scene preventing me from doing that? Ignore the robot. These are the names of the objects: wooden cabinet, red block, yellow cube
```
Outputs from VLM:
```
1. There is a red block in front of the wooden cabinet, preventing the robot from opening it.
2. Yes, the red block and the yellow cube are blocking the robot from opening the wooden cabinet.
3. The robot in the simulation is unable to open the wooden cabinet because it is facing the wrong direction. It is facing the cabinet, but the door is closed. The robot is also unable to see the red block and the yellow cube that are located outside of the cabinet.
4. There is no existence of any other object in the scene, apart from the robot, the wooden cabinet, the red block, and the yellow cube.
5. There is a robot standing in front of the wooden cabinet, preventing it from being opened.
6. There is a robot standing in front of the wooden cabinet, preventing you from opening it.
```
LLM Summary:
```
The most probable reason why the robot cannot open the wooden cabinet is that there is a red block in front of the cabinet, blocking its path.
```

**Task 4: VLM is asked to diagnose why the robot cannot open the microwave.**


Input to VLM:

```
1. A robot is in a simulation environment where it can interact with any object like in the real world. The robot would like to open the microwave but it cannot. Is there something in this scene preventing that, other than the robot? Assume the robot can interact with anything. These are the names of the objects in our scene: blue kettle, microwave, cabinet
2. In a simulation, a robot wants to open the microwave but can't. Is anything else, besides the robot, blocking it? Check the objects in the scene: blue kettle, microwave, cabinet.
3. Robot in a simulation wants to open the microwave, can't. Something else stopping it? Objects in scene: blue kettle, microwave, cabinet.
4. A robot can engage with any item. It wants to open the microwave but can't. Is an object in this scene, apart from the robot, hindering it? Objects present: blue kettle, microwave, cabinet
5. I would like to open the microwave but I cannot. Is there something in this scene preventing that, other than the robot? These are the objects in the scene: blue kettle, microwave, cabinet
6. I would like to open the microwave but I am unable to. Is there something in this scene preventing me from doing that? Ignore the robot. These are the names of the objects: blue kettle, microwave, cabinet
```

Outputs from VLM:

```
1. There is a blue kettle in the scene, which is preventing the robot from opening the microwave.
2. In the scene, there is a blue_kettle on the microwave and a cabinet above the microwave. The robot is not blocking the microwave, so the only thing preventing the robot from opening the microwave is the blue_kettle on the microwave.
3. The robot in the scene is unable to open the cabinet because there is a blue_kettle placed on the surface in front of the cabinet. This object is blocking the robot's access to the cabinet, preventing it from opening it.
4. There is no existence of a blue kettle in the image description.
5. There is a blue kettle on the countertop that could be preventing you from opening the microwave.
6. There is a robot standing on the counter in front of the microwave. It is blocking access to the microwave.
```

LLM Summary:
```
The most probable reason why the robot cannot open the microwave is that there is a blue kettle placed on the microwave, blocking the robot's access to it.
```

**In Appendix C3, we also show some error cases of where the interaction betewen the Perceiver and High-Level Planner fails.** As an example, in Task 4, the robot tried to open the microwave door but failed due to a kettle obstructing the path. The Perceiver gave five diagnoses, of which three claimed that the kettle was blocking the way, one claimed the cabinet door was blocking the way, and one did not give any conclusive diagnosis. The summary LLM concluded that it was the cabinet door that blocked the action. The robot went on to interact with the cabinet and never removed the kettle. However, in 9 runs out of 10, the High-LeveL Planner was able to correctly summarize the reason from the VLM outputs.

**PERCEIVER-PLANNER MOTIVATION**

As an additional experiment, we also report the ability of GPT-4V to solve Tasks 3 and 6 out-of-the-box by prompting it to create a plan for opening the cabinet and placing the cube with the same colour as the crate on the crate (Appendix D). In both cases, the generated plans did not provide a working solution. In the first case, plan did not identify that there was a block across the handles of the cabinet the robot needed to first remove, and in the second case, the plan did not specify the name of the cube to use. This shows combining the VLM with specific LLM prompting is essential in order to solve these tasks.

We again thank you for your feedback, please let us know if there are additional analyses you wish to see.

[1] Kirillov A et al, Segment Anything. (2023)

# Rebuttal 1
# Common Response

To all reviewers: 

We sincerely thank all reviewers for their thoughtful comments and feedback. Based on the suggestions, we have made major updates to our manuscript, especially in experimental evaluations. We list the changes below.

### Prompts 

As requested by reviewers, we have provided details of our prompts in  Appendix B and Algorithm 1. We have updated our prompting mechanism which vastly improves the consistency of our model. As a particular example, in our initial draft, we point out that the VLM perceiver was often not reliable enough to produce diagnoses from a single prompt. However, through our updated prompting (Prompts B.9-12), we believe this has been alleviated due to a better prompting scheme between the Planner and Perceiver and is no longer an issue.

### Evaluations

Multiple reviewers point out the lack of breadth and few trials in our initial evaluations. In light of this, we include two new environments and three new tasks. We also provide quantitative evaluations with  increase the number of trials to 10 each for all  each experiment . 

The new environments and tasks are:
- **Wooden cabinet and a lever switch** (Task 5). In this scene, the wooden cabinet is locked via a lever switch. The robot is asked to find the blue cube inside the wooden cabinet. To open the cabinet, the robot needs to perceive and reason about the locking mechanism to figure out the way to activate the lever switch.
- **Colored cubes scene** (Taks 6-7). There are two colored cubes (one yellow and one red) and a colored crate (either yellow or red) in this scene. The goal of this scene is to put the cube that has the same color as the crate on the crate. This environment is designed to evaluate the model’s ability to reason through the visual attributes of the objects, as well as the ability to execute conditional plans (e.g., “If the color of the cube on the left matches the crate, put that cube on the crate”). Two tasks are associated with this scene.
    * *Task 6: The two coloured cubes are placed on opposite sides of the crate.* The robot needs to identify the correct cube and move it on the crate.
    * *Task 7: The cube with the wrong color is initialized on the crate.* The robot will fail to put the correct cube on the crate before removing the wrong one. This task evaluates the model’s ability to diagnose failure through visual attributes reasoning.
    
**In total, our experiments resulted in a total of 401 MPC action calls and 101 VLM action calls, allowing the robot to execute tasks consisting of up to 17 steps.**


### Draft

We have also made multiple changes to our draft, thanks to the suggestions raised by the reviewers. Specifically, we have added more details in the Results and Appendix on experimental error analysis. We also include ablations of our Perceiver and MPC module. Finally, we include baseline experiment plans from a non-LLM-based method (PDDL) and vanilla GPT-4V (released on Nov 5 2023, after the submission).  

The detailed changes in the main text are:

- We have fixed multiple typos, phrasing issues, and formatting issues.
- We have reworked the Introduction and Related Works section to provide a better motivation for our work.
- We have updated Algorithm 1. We fixed a few errors and added links to the exact prompts.
- In the Experiment section, we added the descriptions of the new environments and new tasks. We also updated Sec 4.4 to provide a more thorough results analysis.
- In the Discussion and Limitations section, we updated our comments on the VLM perceiver given our latest evaluations. We also included some discussions on the newly released GPT-4V model.
- We have also provided more details in the Appendix. The detailed additions are:
- In Appendix A, we provide the initial and finished scene images, as well as the text instructions for each of the tasks.
- In Appendix B, we list  the exact prompts that we use in our model and a walk-through. The readers can also cross-reference Algorithm 1.
- In Appendix C, we provide more details on our model, including the distribution of VLM and MPC steps in the 7 tested tasks, additional experiments verifying the motion planning module, an ablation study for different VLM models, and finally the error case analyses of our model. We also test 3 state-of-the-art VLMs, including GPT-4V. 
- In Appendix D, we provide an additional experiment to test the ad-hoc planning ability of GPT-4V.
- In Appendix E, we provide a comparison between our model and TAMP.


# Response to Reviewer jtHn

Thank you for your feedback, and we are glad that you find our work interesting. To address your concerns:

**1. More environments and tasks.** We have included two new environments in this iteration with three new tasks. A description can be found in Section 4.1 and the results in Table 1. The environments are a cabinet scene with a lever that unlocks the cabinet (the cabinet is locked when the scene starts). The robot must reason that there are no physical obstacles in the scene and must figure out that pulling the lever unlocks the door in order to accomplish the task. The other environment contains a red crate with two blocks, one red and one yellow. The first task in this environment is to place the cube with the same colour as the crate on the crate, and so the robot must use the Perceiver to determine the correct cube (otherwise it could just get 50% by guessing). The final task is in the same environment; the robot must put the red cube on the crate but there is a yellow cube already on the crate, preventing placement of the red block. The robot must first remove the yellow cube. In addition to integrating these new tasks, we also increased the number of times we ran each of the 7 tasks (from 3 to 10) to further investigate the robustness of our method.

**2. Testing planner module.** For each important motion in our tasks, we first do multiple MPC runs with ground-truth reward functions to test the stability. We pick four of the important motions and include the test results in Appendix C.1 in our updated draft.

**3. VLM Perceiver Weakness.** We thank the reviewer for their suggestion. We have updated our prompting mechanism to alleviate the problems incurred by the VLM perceiver (the reviewer can refer to Appendix B in our updated draft for the detailed prompts, as well as Algorithm 1). As a result, we believe the VLM perceiver is no longer a dominant issue. In Table 1, we demonstrate the impact of only providing a text description of the objects in the scene at start-up (RePLaNn [no Perceiver]), which significantly drops the performance. We include experiments in Appendix C2 demonstrating the object recognition and object reasoning skills of two open-source VLMs and GPT-4V. We find that combining open-source models with segmentation models can also boost object recognition capabilities (Section 4.2 and Appendix C2). We include an additional example of why LLM-constrained VLM prompting is important for our method to work in Appendix D, where we ask GPT-4V to solve Tasks 3 and 6 out-of-the-box, and find that it is unable to. Finally, we include a more in-depth error analysis of the failure cases in Appendix C3 and find that there are still some small issues with the Perceiver-Planner interaction, which could be improved with better prompting. For example, in Task 6, the Planner asks the Perceiver “Which cube has the same colour?” which is very vague, and the Perceiver ends up reply nonsensically and the robot picks the wrong cube.


# Response to Reviewer b9qX

We appreciate the reviewer’s detailed comments and suggestions. We address the reviewer’s specific concerns below:

**1. Missing information about VLM Perceiver.** We apologize for not providing sufficient details about the VLM Perceiver. We have updated our draft to include all prompts used by the Planners, Perceiver, and Verifier in Appendix B and annotate each line in Algorithm 1 with corresponding prompts. In developing our framework, our experiences completely align with your comments that vanilla, off-the-shelf VLMs are not sufficient to solve these tasks. As a motivating example, we naively ask GPT-4V (which has demonstrated amazing vision-language capabilities) to create a plan for solving Tasks 3 and 6 (the reviewer can find these in Appendix D). In both cases, GPT-4V was unable to identify the correct plan. For Task 3, it was unable to identify that there was block in between the cabinet handles and in Task 4 it could not name the correct cube right off the bat. However, one key insight we find in this work is that we can use an LLM (which has better reasoning skills) to determine what questions it should ask the VLM in a constrained, VQA-style setting in order to update its understanding of the environment. For example, if the Planner asks the  VLM: “Robot in a simulation wants to open the microwave door, can’t. Something else stopping it?” The VLM is able to provide the caption that there is a kettle in front of the door. To prevent overfitting to one question, the LLM asks the VLM multiple iterations of the question (see Figure B9 in Appendix B) and then based on its own knowledge of the world state, it provides a summary answer based on feedback from the VLM of what the most likely explanation. The Planner can also query the Perceiver to update its knowledge on object states, for example: “What is the colour of the crate?” or “Do you see a green apple?” (see Figures B14-B16 in Appendix B). We find that combining LLMs to effectively prompt VLMs and using Verifiers to make sure their outputs are consistent are essential for VLMs to provide useful feedback. We also provide an ablation study in Appendix C2 on the performance of two open-source models and GPT-4V on object recognition and reasoning. 

**2. Limited evaluation complexity.** We apologize for the unclear writing and have fixed our description of the number of actions the robot does to accomplish each goal. When we consider an action as any time the robot needs to do something to satisfy an MPC reward or decides to call the VLM to obtain information, most tasks requires on average between 7 and 11 subtasks without human intervention (with some runs using up to 17 depending on how the robot decides to replan). We show a Table in Appendix C (Figure C1) breaking down the number of MPC actions vs VLM actions for each task. This is comparable to the number of actions required by the longest-horizon tasks in SayCan, but those tasks do not have obstacles preventing the robot from following the high-level instructions and do not require replanning based on environment feedback. Furthermore, SayCan utilizes primitive skills to maintain a reliable subtask execution, which we do not assume to have. Not using primitive skills also poses significant challenges to the reliability of the method.
Few evaluation trials. We have increased the number of trials for each task to 10. We have also added two new environments and three more tasks. 

**3. Clarification of core claim.** We apologize for not making the core contributions clear. Our core contribution with this work is as follows: we develop an agent to handle long-term, multi-stage tasks by combining four key aspects: using perceiver models for high-level replanning, creating hierarchical plans with language models, verifying outputs from these language models, and adaptive robot behavior through reward generation. We have updated the introduction to better reflect this. Thank you for pointing out a missing reference to Inner Monologue (IM). While both our works show that robots can perform more complex and longer-horizon tasks by receiving feedback from different sources while executing a task, we are different in the following ways: (a)Their framework uses a VLM at the beginning of the scene for object recognition, but while the scene is progressing, they obtain feedback from humans in some experiments, or an object recognition model to update knowledge about what objects are present/absent in the scene. They do not rely on VLMs for replanning  or providing reasons when the success detector fails. This is most similar to row 3 in our Table 1. Our tasks are not solvable from the presence/absence of objects alone, and our framework is about to solve them without human intervention. (b) When the success detector fails, the robot retries or selects another low-level policy to redo the task (not necessarily to determine reasons why the task failed based on the scene state). 

**4. Failure reasons.** In the updated draft, we include the following additional analyses. In Appendix C1, we test the motion controller module on common motions done in our experiments to ensure it is consistent. We rerun these motions 20 times each. For the errors that occur in our full model framework, we add an analysis of the errors briefly in the Results section in more detail in Appendix C3. The main errors were from the Perceiver-Planner interaction in diagnosing possible failure reasons. In one failed case, the Perceiver did in fact correctly identify the kettle was blocking the microwave in 4/6 questions, but in 1 response it postulated that the cabinet might be the problem. The Planner then replanned for the robot to move the cabinet out of the way instead of the kettle. In another error case for Task 6, the Planner asked the Perceiver “Which cube has the same colour?” which is vague and does not provide enough context, and so the Perceiver did not reply sensibly. In Task 3, the errors were mainly due to MPC failures of the robot trying to remove the red block since it is an intricate action. 


# Response to Reviewer Ya9u

We are glad that the reviewer finds our idea clear and well-motivated. We also appreciate the reviewer’s insightful feedback. Below we address the reviewer’s concerns:

**1. Improved literature review.** We agree with the reviewer’s observation that the literature review could be improved. We have updated our introduction section and related works section to provide a more thorough and structured literature review, and have re-written sections to better put our work in the context of previous papers and ideas.

**2. Lack of thorough experiments and comparison with prior non-LLM works.** We appreciate the reviewer for their suggestion to improve our evaluations. We have worked on both of the axes by including three new tasks in two new scenes and increasing the number of runs from three to ten. One of the new tasks involves reasoning about an interaction with a lever-locking mechanism, and the other new tasks emphasize perceiving and reasoning about colors and executing conditional plans. We believe the inclusion of these new tasks can improve the variety of our evaluation. We also improve the analysis of existing tasks in the Results section and  Appendix C by analyzing the number of actions required by our tasks (401 MPC actions + 101 VLM actions in total), as well as analyzing common errors. Finally, we include two additional baselines for a few tasks: PDDL (Appendix E) and GPT-4V (Appendix D). We find that out-of-the-box, GPT-4V is not able to solve our tasks. We include a PDDL domain and problem definition for Tasks 1-3. We show that they are not trivial to write and rely on human-provided ground truth information (for example, that the door is blocked). We also consider using PDDLStream to solve the problem. In this case, some of the human-provided ground-truth information is certified by Stream (by using a motion planner it verifies the feasibility of action execution at the planning time); however, it’s inefficient to solve. In terms of hierarchical RL, our work assumes text-based goals with no access to pretrained skills. To the best of our knowledge, there are no prior RL works that succeed under these assumptions. The closest one we find is [1], which is only evaluated in single-motion tasks, while the majority of our tasks are multi-step. In addition, our algorithm can be evaluated on the fly while RL methods typically require extensive training, which we believe can be an unfair comparison.

**3. Simulator ground truth for MPC.** We believe using simulator ground truth is a necessary compromise to make, and is currently what state-of-the-art works use (Language to Rewards [2], Inner Monologue [3]). Using a ground truth simulator does not jeopardize transferring the system to the real world. For example, [2] and [3] utilize a vision model to map the real-world objects to the simulator and then execute the simulator-produced actions in real world. Another possible way could be collect demonstrations in the simulator and distill an image-based policy from them, which can be directly applied to the real world.

**4. The introduction reads more like related works.** We apologize for the possible confusion. We have updated our introduction to provide a better motivation for our work. We have also fixed the citation formatting issue. We thank the reviewer for pointing this out.

**5. Missing prompts.** We have included the detailed prompts in Appendix B in our updated manuscript, as well as how they are used in Algorithm 1 and throughout the text. We hope it can provide enough information for the reviewer.

[1] Wenhao Yu et al, “Language to Rewards for Robotic Skill Synthesis”. (2023)
[2] Tianbao Xie et al, “Text2Reward: Automated Dense Reward Function Generation for Reinforcement Learning”. (2023)
[3] Wenlong Huang et al, “Inner Monologue: Embodied Reasoning through Planning with Language Models”. (2023)


# Response to Reviewer 6UHr

Thank you very much for your feedback! To address your concerns:

**1. Missing prompts.** We apologize for not originally including the prompts used. In our updated draft, we have included all prompts used in Appendix B and indicate how they are used in Algorithm 1 and throughout the text. 

**2. Limited evaluations.** We agree with the reviewer that our experiment setting was limited. We have increased the number of experiments on two fronts. The first is that we added two new environments and three new tasks. The first added environment contains a cabinet that is initially locked, and a lever. The robot has to open the cabinet to deposit an item in there, but it cannot. There is also nothing physically blocking it, and so the robot has to reason that it must somehow interact with the lever in order to unlock the cabinet. The second environment is a scene with a red crate and two cubes, one red and one yellow. In the first task, the robot must place the cube with the same colour as the crate on the crate. This requires conditional reasoning with the Perceiver. The second tasks also requires the robot to place the red cube on the crate, but this time there is a physical blocker: the yellow cube is already on the crate, and there is only room for one. Thus, the robot must first remove the yellow cube in order to accomplish the task. We have also increased the number of runs of each experiment from 3 to 10. We also include a breakdown of the number of actions the robot does in order to do the tasks. In total, the robot performs 401 MPC actions and 101 VLM actions. This is more than previous work (L2R, 170 MPC actions). To address the reviewer’s concerns about experimental errors, we include a more thorough breakdown of model components and an error analysis (see Appendix C2 and C3).  

**3. Prior work.** Thank you to the reviewer for pointing out these prior works. Indeed, we include many of them in our updated draft to clarify our contributions. Specifically, Inner Monologue (IM) does not actively use feedback from VLMs to replan during a task; it uses feedback from humans or object recognition models to know what objects are present or absent (this would be most similar to row 3 in our Table 1). IM uses a success detector in order to determine whether it could resample the low-level policy for a pick-and-place action, but it would not be able to handle obstacles and does not reason on why the action failed. It has been shown in works since binary success detection is not enough to perform complex tasks [1]. Voyager also shows this by receiving feedback from a chat bot in the Minecraft API on why code execution errors were generated. They (in addition to other works) also show that Verifiers are important for long-horizon task completion. We agree that the use of Verifiers and environment feedback is extremely important. We have added these references to our Related Works section. Towards a Unified Agent relies on previously collected observations to finetune a CLIP reward model. Our method does not require any model training. We believe our method is novel because we combine several key insights in order to execute complicated, long-horizon tasks without the need for model re-training or human guidance. First, we use perceiver models to diagnose problems and gather information about object states in order to replan if the robot encounters issues. We create hierarchical plans for both high-level reasoning and low-level reward generation, allowing for adaptive robot behaviour that has only been previously shown on single-step tasks. Finally, we use verifiers at both high-level and low-level reasoning to enable long-horizon tasks of up to 17 steps. 

**4. Replanning iterations.** Our replanning framework is set up recursively, so that the agent can replan when performing at any subtask. However, we set a threshold on recursion depth (r=2) and the number of replanning stages allowed (p=2) to lower runtime and API call costs. 

**5. Failure cases.** We include a more in-depth error analysis of all components in our system in this draft. Specifically, we showcase Perceiver errors in Appendix C3. The Perceiver is often able to successfully identify what the reason for plan failure is, but sometimes the interaction between the Perceiver and Planner results in the wrong summary reason. We also include an ablation study in C2 that reports the object recognition and object reasoning performance of two state-of-the-art open-source VLMs and GPT-4V. 

**6. Citations formatting.** We apologize for the citation issue and have fixed it in the current draft, we thank the reviewer for pointing it out to us. 


[1] Lei Wang et al, “A Survey on Language Language Model based Autonomous Agents”. (2023) 
